A machine learning algorithm is a set of rules or steps that a computer follows to learn from data and make categorization, decisions or predictions.
Think of it like teaching a child to recognize animals by showing them lots of pictures. Over time, the child learns to tell the difference between a cat and a dog. Machine learning works in a similar way—by learning patterns from examples.
Classification algorithms sort data into categories or groups.
Example: Think of your email inbox. A classification algorithm helps decide whether a new email is **spam** or **not spam** by learning from examples of past emails.
Clustering algorithms group similar data points together, even if you don’t tell them what the groups should be.
Example: An online store might use clustering to find groups of customers who like similar products. This helps the store recommend items they might enjoy.
Use Aidy’s built-in tools to convert, clean, and structure raw data into an analysis-ready format. You can aggregate data, engineer features, merge, split, filter, and join datasets to prepare them for model training or evaluation.
The F-score is a metric used to evaluate the performance of a machine learning model in classification. After building a model using Aidy, the F-score and related metrics will be displayed. A higher F-score indicates a better model.
When one class in your dataset significantly outnumbers the other (e.g., spam emails making up only 1% of all emails), the dataset is considered imbalanced.
Under-sampling is a technique automatically applied by Aidy to balance the dataset, enabling more accurate and effective models without the need for manual preprocessing.
Regression algorithms are used to predict a number based on past data.
Example: Imagine you're trying to guess how much money your store will make next year. A regression algorithm looks at your past sales and helps you predict next year’s sales.
R-squared, also known as the coefficient of determination, measures how well a regression model explains the variation in the target variable. It typically ranges from 0 to 1, where 0 means the model explains none of the variation and 1 means perfect fit.
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